Sorting and recognition problems for ordered sets
Proceedings on STACS 85 2nd annual symposium on theoretical aspects of computer science
Exploiting Pseudo Models for TBox and ABox Reasoning in Expressive Description Logics
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Optimizing Terminological Reasoning for Expressive Description Logics
Journal of Automated Reasoning
Optimized Reasoning in Description Logics Using Hypertableaux
CADE-21 Proceedings of the 21st international conference on Automated Deduction: Automated Deduction
High performance reasoning with very large knowledge bases: a practical case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Parallel TBox Classification in Description Logics --First Experimental Results
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Optimising ontology classification
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Foundations of description logics
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Web Semantics: Science, Services and Agents on the World Wide Web
A novel approach to ontology classification
Web Semantics: Science, Services and Agents on the World Wide Web
Reasoning-supported interactive revision of knowledge bases
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Hi-index | 0.00 |
One of the core services provided by OWL reasoners is classification : the discovery of all subclass relationships between class names occurring in an ontology. Discovering these relations can be computationally expensive, particularly if individual subsumption tests are costly or if the number of class names is large. We present a classification algorithm which exploits partial information about subclass relationships to reduce both the number of individual tests and the cost of working with large ontologies. We also describe techniques for extracting such partial information from existing reasoners. Empirical results from a prototypical implementation demonstrate substantial performance improvements compared to existing algorithms and implementations.